Open AccessApplication of surface‐enhanced laser desorption/ionization time‐of‐flight‐based serum proteomic array technique for the early diagnosis of prostate cancer
Author(s) -
Pan YuZhuo,
Xiao XueYuan,
Zhao Dan,
Zhang Ling,
Ji GuoYi,
Li Yang,
Yang BaoXue,
He DaCheng,
Zhao XueJian
Publication year - 2006
Publication title -
asian journal of andrology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.701
H-Index - 74
eISSN - 1745-7262
pISSN - 1008-682X
DOI - 10.1111/j.1745-7262.2006.00103.x
Subject(s) - time of flight mass spectrometry , prostate cancer , diagnostic model , mass spectrometry , surface enhanced laser desorption/ionization , proteomics , matrix assisted laser desorption/ionization , mass spectrum , blood proteins , bioinformatics , ionization , medicine , chemistry , computational biology , cancer , chromatography , biology , desorption , computer science , biochemistry , data mining , tandem mass spectrometry , protein mass spectrometry , ion , organic chemistry , adsorption , gene
Aim: To identify the serum biomarkers of prostate cancer (PCa) by protein chip and bioinformatics. Methods: Serum samples from 83 PCa patients and 95 healthy men were taken from a mass screening in Changchun, China. Protein profiling was carried out using surface‐enhanced laser desorption/ionization time‐of‐flight mass spectrometry (SELDI‐TOF MS). The data of spectra were analyzed using two bioinformatics tools. Results: Eighteen serum differential proteins were identified in the PCa group compared with the control group ( P < 0.01). There were four proteins at the higher serum level and 14 proteins at the lower serum level in the PCa group. A decision tree classification algorithm that used an eight‐protein mass pattern was developed to correctly classify the samples. A sensitivity of 92.0 % and a specificity of 96.7 % for the study group were obtained by comparing the PCa and control groups. Conclusion: We identified new serum biomarkers of PCa. SELDI‐TOF MS coupled with a decision tree classification algorithm will provide a highly accurate and innovative approach for the early diagnosis of PCa.